Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching

• URL: http://arxiv.org/abs/2107.00689v1
• Date: Thu, 1 Jul 2021 18:31:42 GMT
• Title: Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching
• Authors: Youngjoo Kim
• Abstract summary: The proposed system attempts label-to-label matching, not image-to-image matching between aerial images and a map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem.
• Score: 1.6244541005112747
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching between aerial images and a map database. By using semantic segmentation, the ground objects are labelled and the configuration of the objects is used to find the corresponding location in the map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem. This paper proposes a pattern matching algorithm which does not require altitude information or a camera model to estimate the absolute horizontal position. The feasibility analysis with simulated images shows the proposed map-based navigation can be realized with the proposed pattern matching algorithm and it is able to provide positions given the labelled objects.

Related papers

• Image Matching Filtering and Refinement by Planes and Beyond [8.184339776177486]
  This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2024-11-14T14:37:50Z)
• Tightly-Coupled, Speed-aided Monocular Visual-Inertial Localization in Topological Map [0.7373617024876725]
  This paper proposes a novel algorithm for vehicle speed-aided monocular visual-inertial localization using a topological map. The proposed system aims to address the limitations of existing methods that rely heavily on expensive sensors like GPS and LiDAR.
  arXiv  Detail & Related papers  (2024-11-08T11:55:27Z)
• DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions [29.828201168816243]
  We investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map. We contribute an algorithm which refines an initial set of candidate object detections and produces a refined subset of highly accurate detections using a prior map.
  arXiv  Detail & Related papers  (2023-06-30T10:46:51Z)
• Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation [53.47160182465171]
  Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images. The authors propose a novel few-shot rotation-invariant aerial semantic segmentation network (FRINet) Experiments demonstrate that FRINet achieves state-of-the-art performance in few-shot aerial semantic segmentation benchmark.
  arXiv  Detail & Related papers  (2023-05-29T09:28:34Z)
• Location-Aware Self-Supervised Transformers [74.76585889813207]
  We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T16:24:29Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Semantic Image Alignment for Vehicle Localization [111.59616433224662]
  We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera. In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
  arXiv  Detail & Related papers  (2021-10-08T14:40:15Z)
• Compositional Sketch Search [91.84489055347585]
  We present an algorithm for searching image collections using free-hand sketches. We exploit drawings as a concise and intuitive representation for specifying entire scene compositions.
  arXiv  Detail & Related papers  (2021-06-15T09:38:09Z)
• Image Retrieval for Structure-from-Motion via Graph Convolutional Network [13.040952255039702]
  We present a novel retrieval method based on Graph Convolutional Network (GCN) to generate accurate pairwise matches without costly redundancy. By constructing a subgraph surrounding the query image as input data, we adopt a learnable GCN to exploit whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on the challenging dataset of highly ambiguous and duplicated scenes.
  arXiv  Detail & Related papers  (2020-09-17T04:03:51Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.